Qwen3.5 Flash vs Qwen3.8-27B

Updated September 28, 2026. Rank says Qwen3.8-27B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.8-27B has the higher public score estimate, 55.26 versus 45.49, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Qwen3.5 Flash

Alibaba

45.49/100

Estimated · Public rank #99

90% interval 36.5–54.5

Model B

Qwen3.8-27B

Alibaba

55.26/100

Estimated · Public rank #58

90% interval 47.7–62.8

Shared results: 4
Qwen3.5 Flash only: 2
Qwen3.8-27B only: 29
Like-for-like categories: 0 / 8

Estimated: Qwen3.5 Flash and Qwen3.8-27BHow the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
Qwen3.5 Flash
Qwen3.5 Flash has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.5 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Qwen3.5 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

26.4Qwen3.5 Flash48.7Qwen3.8-27B

Directional only · BenchAlign v5.7

Qwen3.8-27B scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

LiveCodeBench (Vals)Coding
Normalized gap 0.7
Qwen3.5 Flash:83.3%
Qwen3.8-27B:84.0%
Qwen3.5 Flash source Qwen3.8-27B source
MMLU-Pro (Vals)Knowledge
Normalized gap 0.2
Qwen3.5 Flash:84.1%
Qwen3.8-27B:84.3%
Qwen3.5 Flash source Qwen3.8-27B source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Coding

Directional only

Qwen3.5 Flash: 26.4; Estimated · #103/142
Qwen3.8-27B: 48.7; Supported · #45/142
Basis: BenchAlign v5.7 lane · 2 vs 8 public rows
Reading: Directional only

Knowledge

Directional only

Qwen3.5 Flash: 43.1; Estimated · #85/168
Qwen3.8-27B: 49.2; Supported · #63/168
Basis: BenchAlign v5.7 lane · 2 vs 6 public rows
Reading: Directional only

Agentic

Not comparable

Qwen3.5 Flash: Not ranked
Qwen3.8-27B: 61.2; Supported · #18/117
Basis: BenchAlign v5.7 lane · 0 vs 8 public rows
Reading: Not comparable

Reasoning

Not comparable

Qwen3.5 Flash: Not ranked
Qwen3.8-27B: 78.7; #8/27
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Qwen3.5 Flash: Not ranked
Qwen3.8-27B: 80.9; #11/50
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Qwen3.5 Flash: Not ranked
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Qwen3.5 Flash: Not ranked
Qwen3.8-27B: 83.2; #45/124
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Qwen3.5 Flash: 28.4; Unranked · 2 rankable rows
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Qwen3.5 Flash and Qwen3.8-27B
Category	Qwen3.5 Flash	Qwen3.8-27B	Basis	Reading
Coding	26.4Estimated · #103/142	48.7Supported · #45/142	Directional onlyBenchAlign v5.7 lane · 2 vs 8 public rows	Directional only
Knowledge	43.1Estimated · #85/168	49.2Supported · #63/168	Directional onlyBenchAlign v5.7 lane · 2 vs 6 public rows	Directional only
Agentic	Not ranked	61.2Supported · #18/117	Not comparableBenchAlign v5.7 lane · 0 vs 8 public rows	Not comparable
Reasoning	Not ranked	78.7#8/27	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	80.9#11/50	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	83.2#45/124	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	28.4Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Qwen3.5 Flash: $0.0003; Fits in one request
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.8-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.5 Flash: $0.0062; Fits in one request
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.8-27B has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Qwen3.5 Flash: $0.026; Fits in one request; Cached input priced at the published list-input rate
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-27B has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationQwen3.5 FlashQwen3.8-27B

Context window

Maximum documented context; output-token limits may be lower.

Qwen3.5 Flash

Qwen3.8-27B

262K

Qwen3.8-27B model card

API model ID

Qwen3.5 Flash

Not sourced

Qwen3.8-27B

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Qwen3.5 Flash

Not published

Qwen3.8-27B

No comparable hosted API rate

Qwen3.8-27B model card

Documented inputs

Qwen3.5 Flash

Not sourced

Qwen3.8-27B

Not sourced

Documented outputs

Qwen3.5 Flash

Not sourced

Qwen3.8-27B

Not sourced

Provider availability

Qwen3.5 Flash

Not sourced

Qwen3.8-27B

Not sourced

Reasoning profile

Qwen3.5 Flash

Reasoning

Qwen3.8-27B

Reasoning

Weight access

Qwen3.5 Flash

Proprietary

Qwen3.8-27B

Open Weight

License

Qwen3.5 Flash

Proprietary

Qwen3.8-27B

Open Weight

Release date

Qwen3.5 Flash

2026-03-04

Qwen3.8-27B

2026-08-05

If you already use one of these models

Deployment change: Both entries list Alibaba as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal: Qwen3.8-27B has the higher public score estimate, 55.26 versus 45.49, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.5 Flash has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Qwen3.5 Flash or Qwen3.8-27B?

Qwen3.8-27B has the higher public score estimate, 55.26 versus 45.49, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Qwen3.5 Flash or Qwen3.8-27B?

Qwen3.8-27B scores higher for coding on the public lane, 48.7 to 26.4. Qwen3.5 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Qwen3.5 Flash or Qwen3.8-27B?

Qwen3.5 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Qwen3.5 Flash or Qwen3.8-27B?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Qwen3.5 Flash or Qwen3.8-27B?

Qwen3.5 Flash has the larger documented context window: 1M, compared with 262K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence35 rows

Agentic

Terminal-Bench 2.1
Qwen3.5 Flash—
Qwen3.8-27B73.0%
Source
Not directly comparable
CoWorkBench
Qwen3.5 Flash—
Qwen3.8-27B70.7%
Source
Not directly comparable
JobBench
Qwen3.5 Flash—
Qwen3.8-27B33.4%
Source
Not directly comparable
Agents' Last Exam
Qwen3.5 Flash—
Qwen3.8-27B42.9%
Source
Not directly comparable
OSWorld-Verified
Qwen3.5 Flash—
Qwen3.8-27B84.3%
Source
Not directly comparable
WebArena-Verified
Qwen3.5 Flash—
Qwen3.8-27B64.8%
Source
Not directly comparable
AndroidWorld
Qwen3.5 Flash—
Qwen3.8-27B81.9%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Qwen3.5 Flash—
Qwen3.8-27B58.4%
Source
Not directly comparable

Coding

LiveCodeBench (Vals)
Qwen3.5 Flash83.3%
Source
Qwen3.8-27B84.0%
Source
Qwen3.8-27B leads this result
SWE-bench (Vals)
Qwen3.5 Flash64.4%
Source
Qwen3.8-27B86.0%
Source
Qwen3.8-27B leads this result
Terminal-Bench 2.1
Qwen3.5 Flash—
Qwen3.8-27B73.0%
Source
Not directly comparable
SWE-bench Pro
Qwen3.5 Flash—
Qwen3.8-27B61.7%
Source
Not directly comparable
NL2Repo
Qwen3.5 Flash—
Qwen3.8-27B42.3%
Source
Not directly comparable
DeepSWE
Qwen3.5 Flash—
Qwen3.8-27B42.2%
Source
Not directly comparable
LiveCodeBench v6
Qwen3.5 Flash—
Qwen3.8-27B90.3%
Source
Not directly comparable
VulcanBench v3
Qwen3.5 Flash—
Qwen3.8-27B82.6%
Source
Not directly comparable

Multimodal

MathVision
Qwen3.5 Flash—
Qwen3.8-27B90.0%
Source
Not directly comparable
MathVision w/ Python
Qwen3.5 Flash—
Qwen3.8-27B94.6%
Source
Not directly comparable
BabyVision
Qwen3.5 Flash—
Qwen3.8-27B65.7%
Source
Not directly comparable
BabyVision w/ Python
Qwen3.5 Flash—
Qwen3.8-27B85.6%
Source
Not directly comparable
Vision2Web
Qwen3.5 Flash—
Qwen3.8-27B62.9%
Source
Not directly comparable
CharXiv w/o tools
Qwen3.5 Flash—
Qwen3.8-27B83.7%
Source
Not directly comparable
CharXiv
Qwen3.5 Flash—
Qwen3.8-27B90.2%
Source
Not directly comparable
OmniDocBench 1.5
Qwen3.5 Flash—
Qwen3.8-27B91.1%
Source
Not directly comparable
RealWorldQA
Qwen3.5 Flash—
Qwen3.8-27B85.9%
Source
Not directly comparable
ERQA
Qwen3.5 Flash—
Qwen3.8-27B65.5%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Qwen3.5 Flash82.8%
Source
Qwen3.8-27B88.9%
Source
Qwen3.8-27B leads this result
MMLU-Pro (Vals)
Qwen3.5 Flash84.1%
Source
Qwen3.8-27B84.3%
Source
Qwen3.8-27B leads this result
GPQA
Qwen3.5 Flash—
Qwen3.8-27B89.2%
Source
Not directly comparable
GPQA-D
Qwen3.5 Flash—
Qwen3.8-27B89.2%
Source
Not directly comparable
HLE
Qwen3.5 Flash—
Qwen3.8-27B30.8%
Source
Not directly comparable
HLE w/o tools
Qwen3.5 Flash—
Qwen3.8-27B30.8%
Source
Not directly comparable

Instruction following

IFBench
Qwen3.5 Flash—
Qwen3.8-27B79.5%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Qwen3.5 Flash6.207%
Source
Qwen3.8-27B—
Not directly comparable
FrontierMath v2 (Tier 4)
Qwen3.5 Flash0.000%
Source
Qwen3.8-27B—
Not directly comparable

35 public results · 4 shared

Related comparisons

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Last updated September 28, 2026

Qwen3.5 Flash vs Qwen3.8-27B

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Qwen3.5 Flash or Qwen3.8-27B?

Which is better for coding, Qwen3.5 Flash or Qwen3.8-27B?

Which is better for agentic tasks, Qwen3.5 Flash or Qwen3.8-27B?

Which costs less, Qwen3.5 Flash or Qwen3.8-27B?

Which has the larger context window, Qwen3.5 Flash or Qwen3.8-27B?

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Qwen3.5 Flash vs Qwen3.8-27B